Graph neural networks for materials science and chemistry
Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …
and materials science, being used to predict materials properties, accelerate simulations …
Unsupervised learning methods for molecular simulation data
Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …
amounts of data produced by atomistic and molecular simulations, in material science, solid …
Bottom-up coarse-graining: Principles and perspectives
Large-scale computational molecular models provide scientists a means to investigate the
effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship …
effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship …
Two decades of Martini: Better beads, broader scope
The Martini model, a coarse‐grained force field for molecular dynamics simulations, has
been around for nearly two decades. Originally developed for lipid‐based systems by the …
been around for nearly two decades. Originally developed for lipid‐based systems by the …
Torchmd-net: equivariant transformers for neural network based molecular potentials
The prediction of quantum mechanical properties is historically plagued by a trade-off
between accuracy and speed. Machine learning potentials have previously shown great …
between accuracy and speed. Machine learning potentials have previously shown great …
Perspective: Advances, challenges, and insight for predictive coarse-grained models
WG Noid - The Journal of Physical Chemistry B, 2023 - ACS Publications
By averaging over atomic details, coarse-grained (CG) models provide profound
computational and conceptual advantages for studying soft materials. In particular, bottom …
computational and conceptual advantages for studying soft materials. In particular, bottom …
Two for one: Diffusion models and force fields for coarse-grained molecular dynamics
Coarse-grained (CG) molecular dynamics enables the study of biological processes at
temporal and spatial scales that would be intractable at an atomistic resolution. However …
temporal and spatial scales that would be intractable at an atomistic resolution. However …
TorchMD: A deep learning framework for molecular simulations
Molecular dynamics simulations provide a mechanistic description of molecules by relying
on empirical potentials. The quality and transferability of such potentials can be improved …
on empirical potentials. The quality and transferability of such potentials can be improved …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Application advances of deep learning methods for de novo drug design and molecular dynamics simulation
De novo drug design is a stationary way to build novel ligands in the confined pocket of
receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation …
receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation …